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• W3131068636 abstract "This paper presents a new coordination algorithm for decentralised multi-robot information gathering. We consider planning for an online variant of the multi-agent orienteering problem with neighbourhoods. This formulation closely aligns with a number of important tasks in robotics, including inspection, surveillance, and reconnaissance. We propose a decentralised variant of the self-organising map (SOM) learning procedure, named Dec-SOM, which efficiently plans sequences of waypoints for a team of robots. Decentralisation is achieved by performing a distributed allocation scheme jointly with a series of SOM adaptations. We also offer an efficient heuristic to select when to perform negotiations, which reduces communication resource usage. Simulation results in two settings, including an infrastructure inspection scenario with a real-world dataset of oil rigs, demonstrate that Dec-SOM outperforms baseline methods and other SOM variants, is competitive with centralised SOM, and is a viable solution for decentralised information gathering." @default.
• W3131068636 created "2021-03-01" @default.
• W3131068636 creator A5023976625 @default.
• W3131068636 creator A5037572995 @default.
• W3131068636 date "2020-10-24" @default.
• W3131068636 modified "2023-09-27" @default.
• W3131068636 title "Decentralised Self-Organising Maps for Multi-Robot Information Gathering" @default.
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• W3131068636 doi "https://doi.org/10.1109/iros45743.2020.9341106" @default.
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